By Topic

Computational functional genomics

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Liang, M.P. ; Dept. of Genetics, Stanford Univ. Medical Center, CA, USA ; Troyanskaya, O.G. ; Laederach, A. ; Brutlag, D.L.
more authors

The exponential growth of the publicly available data has transformed biology into an information rich science that provides new and interesting applications for the machine learning community. In this article, the author presents some specific examples regarding the possibility of representing biological data in a machine-learning framework as well as the contributions these representations impart to both the prediction and discovery of the biological function. The paper also illustrates the proper feature selection critical to the success of the of a particular computational functional genomics approach.

Published in:

Signal Processing Magazine, IEEE  (Volume:21 ,  Issue: 6 )